from re import M
import paddle
from paddle.fluid.layers.nn import pad
from paddle.hapi import model
import paddle.nn as nn
import math
# import torch.utils.model_zoo as model_zoo
from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict







def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    n=9*out_planes
    return nn.Conv2D(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)),bias_attr=False)


class BasicBlock(nn.Layer):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2D(
            planes, weight_attr=paddle.framework.ParamAttr( 
                initializer=paddle.nn.initializer.Constant(1)))

        # self.bn1.weight.data.fill_(1)
        # self.bn1.bias_attr.data.zero_()
        self.relu = nn.ReLU()
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2D(
            planes, weight_attr=paddle.framework.ParamAttr(
                initializer=paddle.nn.initializer.Constant(1)))
        # self.bn2.weight.data.fill_(1)
        # self.bn2.bias_attr.data.zero_()
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Layer):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        n = planes
        self.conv1 = nn.Conv2D(
            inplanes, planes, kernel_size=1, weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)), bias_attr=False)
        # m = self.conv1
        # n = 1 * planes
        # m.weight.data.normal_(0, math.sqrt(2. / n))
        self.bn1 = nn.BatchNorm2D(
            planes, weight_attr=paddle.framework.ParamAttr(

                initializer=paddle.nn.initializer.Constant(1.0)))
        # self.bn1.weight.data.fill_(1)
        # self.bn1.bias_attr.data.zero_()
        n = 9 * planes
        self.conv2 = nn.Conv2D(planes, planes, kernel_size=3, stride=stride,
                               padding=1, weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)), bias_attr=False)
        # m = self.conv2
        # n = 3 * 3 * planes
        # m.weight.data.normal_(0, math.sqrt(2. / n))
        self.bn2 = nn.BatchNorm2D(
            planes, weight_attr=paddle.framework.ParamAttr(

                initializer=paddle.nn.initializer.Constant(1)))
        # self.bn2.weight.data.fill_(1)
        # self.bn2.bias_attr.data.zero_()
        n = planes * 4
        self.conv3 = nn.Conv2D(
            planes, planes * 4, kernel_size=1, weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)), bias_attr=False)
        # m = self.conv3
        # n = 1 * planes * 4
        # m.weight.data.normal_(0, math.sqrt(2. / n))
        self.bn3 = nn.BatchNorm2D(
            planes * 4, weight_attr=paddle.framework.ParamAttr(

                initializer=paddle.nn.initializer.Constant(1)))
        # self.bn3.weight.data.fill_(1)
        # self.bn3.bias_attr.data.zero_()
        self.relu = nn.ReLU()
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Layer):

    def __init__(self, block, layers, num_classes=1000, img_size=256, dropout_factor=0.5):
        self.inplanes = 64
        self.dropout_factor = dropout_factor
        super(ResNet, self).__init__()
        n = 7 * 7 * 64
        self.conv1 = nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)),
                               bias_attr=False)
        # m = self.conv1
        # n = 7 * 7 * 64
        # m.weight.data.normal_(0, math.sqrt(2. / n))
        self.bn1 = nn.BatchNorm2D(64, weight_attr=paddle.framework.ParamAttr(initializer=paddle.nn.initializer.Constant(1)))
        # self.bn1.weight.data.fill_(1)
        # self.bn1.bias_attr.data.zero_()
        self.relu = nn.ReLU()
        # see this issue: https://github.com/xxradon/PytorchToCaffe/issues/16
        # self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
        self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, ceil_mode=True)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        assert img_size % 32 == 0
        pool_kernel = int(img_size / 32)
        self.avgpool = nn.AvgPool2D(pool_kernel, stride=1, ceil_mode=True)

        self.dropout = nn.Dropout(self.dropout_factor)

        # print('\n123\n')
        # print(block.expansion)
        k = math.sqrt(1.0 / (512 * block.expansion))
        self.fc = nn.Linear(512 * block.expansion, num_classes, weight_attr=paddle.framework.ParamAttr(
            initializer=paddle.nn.initializer.Uniform(-k, k)), bias_attr=paddle.nn.initializer.Uniform(-k, k))
        # print('\n123\n')
        # print(self.fc)
        # for m in self.modules():
        #     print('aaaaa\nbbbbbbb\n')
        #     print(m)
        #     if isinstance(m, nn.Conv2D):
        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        #         m.weight.data.normal_(0, math.sqrt(2. / n))
        #     elif isinstance(m, nn.BatchNorm2D):
        #         m.weight.data.fill_(1)
        #         m.bias_attr.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        # print(block, planes, blocks)
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            n=planes * block.expansion
            downsample = nn.Sequential(
                nn.Conv2D(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride,weight_attr=nn.initializer.Normal(0, math.sqrt(2. / n)), bias_attr=False),
                nn.BatchNorm2D(planes * block.expansion,weight_attr=paddle.framework.ParamAttr(initializer=paddle.nn.initializer.Constant(1))),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        # print(layers)
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        # x=x.reshape(x.shape[0],
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        # print(x)
        x = paddle.reshape(x, shape=[x.shape[0], -1])
        # x = x.view(x.size(0), -1)
        # print(x)
        x = self.dropout(x)
        # print(x)
        x = self.fc(x)
        # print(x)
        # print(x[0][0])
        # if(x[0][0] == 0):
        #     print('\nsb22\n')
        return x


def load_model(model, pretrained_state_dict):
    model_dict = model.state_dict()
    pretrained_dict = {k: v for k, v in pretrained_state_dict.items() if
                       k in model_dict and model_dict[k].size() == pretrained_state_dict[k].size()}
    model.load_state_dict(pretrained_dict, strict=False)
    if len(pretrained_dict) == 0:
        print("[INFO] No params were loaded ...")
    else:
        for k, v in pretrained_state_dict.items():
            if k in pretrained_dict:
                print("==>> Load {} {}".format(k, v.size()))
            else:
                print("[INFO] Skip {} {}".format(k, v.size()))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    # model = paddle.Model(ResNet(Bottleneck, [3, 4, 6, 3], **kwargs))
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
        # print("Load pretrained model from {}".format(model_urls['resnet50']))
        # pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
        # model = load_model(model, pretrained_state_dict)
        print('noneeeeee')
    return model


if __name__ == "__main__":
    paddle.set_device('cpu')
    # paddle.enable_static()
    # input = paddle.randn([32, 3, 256, 256])
    input = paddle.ones([32, 3, 256, 256])
    # print(input)
    # print(input)
    # model = paddle.Model(
    #     ResNet(Bottleneck, [3, 4, 6, 3], num_classes=2, img_size=256))
    # model.summary((32, 3, 256, 256))

    model = resnet50(False, num_classes=42, img_size=256)
    output = model(input)
    print(output)
